Data from Trial 49, in vivo assay performed from 20220203 - 20220214.
Treat with loperamide at 5 dpf for 24 hours.
Sampling on 2022.02.09, 02.10, 02.14.
At each timepoint:
For individual strains (1,2,3,4,10):
Make 0 to -3 dilutions in 96-well plates, in triplicate (8 fish per
plate)
Plate 10 µL microdrops on big square plates.
8 square plates total per timepoint
For Bc1/Bc2/Bc3/Bc4/Bc10 condition:
Make 3 dilutions in 1.5 mL tubes: 100 µL into 900 µL water –>
100, 10-1, 10-2, 10-3
Spread 3 x 100 µL aliquots of each dilution on LB plates = 12 plates per
fish
144 plates total per timepoint
Put plates at 28C for 2 days, then count colonies.
| Treatment (Strain) | Loperamide Treatment |
|---|---|
| DMSO | |
| Loperamide 10 mg/L | |
| Bc1 | |
| Bc1 | DMSO |
| Bc1 | Loperamide 10 mg/L |
| Bc2 | |
| Bc2 | DMSO |
| Bc2 | Loperamide 10 mg/L |
| Bc3 | |
| Bc3 | DMSO |
| Bc3 | Loperamide 10 mg/L |
| Bc4 | |
| Bc4 | DMSO |
| Bc4 | Loperamide 10 mg/L |
| Bc10 | |
| Bc10 | DMSO |
| Bc10 | Loperamide 10 mg/L |
| Bc1/Bc2/Bc3/Bc4/Bc10 | |
| Bc1/Bc2/Bc3/Bc4/Bc10 | DMSO |
| Bc1/Bc2/Bc3/Bc4/Bc10 | Loperamide 10 mg/L |
# import individual strain data
datacfustrial49<-
readxl::read_xlsx("Trial49_LoperamideZebrafishWaterCFUs.xlsx", sheet="Fish") %>%
drop_na(DF) %>%
mutate(LoperamideTreatment=factor(LoperamideTreatment,
levels=c("None", "DMSO", "Loperamide 10 mg/L"),
labels=c("Control","DMSO", "Loperamide")),
Treatment = factor(Treatment,
levels=c("Bc1","Bc2","Bc3","Bc4","Bc10","Bc1/Bc2/Bc3/Bc4/Bc10")),
Timepoint = case_when(TrialDay == "6" ~ "24 hr treatment",
TrialDay == "7" ~ "Treatment +\n24 hr recovery",
TrialDay == "11" ~ "Treatment +\n5 day recovery"),
Timepoint_day = case_when(TrialDay == "6" ~ "24hr",
TrialDay == "7" ~ "48hr",
TrialDay == "11" ~ "6d")) %>%
mutate(TimepointDay = factor(Timepoint_day,
levels = c("24hr","48hr","6d"),
labels = c("T0","T1","T5")))
# import mix data
datacfusmixtrial49 <-
readxl::read_xlsx("Trial49_LoperamideZebrafishWaterCFUs.xlsx", sheet="FishMix") %>%
drop_na(DF) %>%
mutate(LoperamideTreatment=factor(LoperamideTreatment,
levels=c("None", "DMSO", "Loperamide 10 mg/L"),
labels=c("Control","DMSO", "Loperamide")),
Timepoint = case_when(TrialDay == "6" ~ "24 hr treatment",
TrialDay == "7" ~ "Treatment +\n24 hr recovery",
TrialDay == "11" ~ "Treatment +\n5 day recovery"),
Timepoint_day = case_when(TrialDay == "6" ~ "24hr",
TrialDay == "7" ~ "48hr",
TrialDay == "11" ~ "6d")) %>%
group_by(Fish,Treatment,LoperamideTreatment,FishNum,TrialDay, Timepoint_day, Timepoint) %>%
summarise_all(.funs="mean", na.rm=TRUE) %>%
unite("LoperamideTimepoint", LoperamideTreatment,Timepoint_day, remove=FALSE) %>%
unite("FishID", LoperamideTreatment,Timepoint_day,FishNum, remove=FALSE) %>%
mutate(TimepointDay = factor(Timepoint_day,
levels = c("24hr","48hr","6d"),
labels = c("T0","T1","T5")))
# import water data
watercfustrial49 <-
readxl::read_xlsx("Trial49_LoperamideZebrafishWaterCFUs.xlsx", sheet="Water") %>%
filter(Treatment != "Bc1/Bc2/Bc3/Bc4/Bc10") %>%
mutate(LoperamideTreatment=factor(LoperamideTreatment,
levels=c("None", "DMSO", "Loperamide 10 mg/L"),
labels=c("Control","DMSO", "Loperamide")),
Treatment = factor(Treatment, levels=c("Bc1","Bc2","Bc3","Bc4","Bc10")))
# import strain metadata
straininfo <- readxl::read_xlsx("../../LoperamideStrainInfo.xlsx") %>%
mutate(Strain=recode(Strain, "W6t"="W6"))
| Treatment | Timepoint | Timepoint_day | .y. | group1 | group2 | p | p.adj | p.format | p.signif | method |
|---|---|---|---|---|---|---|---|---|---|---|
| Bc1 | 24 hr treatment | 24hr | CFUs_perFish | DMSO | Loperamide | 0.04206641 | 1 | 0.042 | * | Wilcoxon |
| Bc2 | 24 hr treatment | 24hr | CFUs_perFish | DMSO | Loperamide | 0.02857143 | 1 | 0.029 | * | Wilcoxon |
| Bc2 | Treatment + 24 hr recovery | 48hr | CFUs_perFish | DMSO | Loperamide | 0.02857143 | 1 | 0.029 | * | Wilcoxon |
| Bc1 | Treatment + 5 day recovery | 6d | CFUs_perFish | DMSO | Loperamide | 0.02940105 | 1 | 0.029 | * | Wilcoxon |
Stats are relative to DMSO
## CFUs of Bc1 in mix
## # A tibble: 36 × 8
## .y. group1 group2 p p.adj p.format p.signif method
## <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
## 1 value Control_24hr Control_48hr 0.839 0.84 0.83945 ns Wilco…
## 2 value Control_24hr Control_6d 0.742 0.79 0.74190 ns Wilco…
## 3 value Control_24hr DMSO_24hr 0.0118 0.024 0.01180 * Wilco…
## 4 value Control_24hr DMSO_48hr 0.00287 0.0074 0.00287 ** Wilco…
## 5 value Control_24hr DMSO_6d 0.0000933 0.00067 9.3e-05 **** Wilco…
## 6 value Control_24hr Loperamide_24hr 0.664 0.79 0.66418 ns Wilco…
## 7 value Control_24hr Loperamide_48hr 0.0000933 0.00067 9.3e-05 **** Wilco…
## 8 value Control_24hr Loperamide_6d 0.000836 0.003 0.00084 *** Wilco…
## 9 value Control_48hr Control_6d 0.279 0.36 0.27920 ns Wilco…
## 10 value Control_48hr DMSO_24hr 0.0682 0.1 0.06824 ns Wilco…
## # … with 26 more rows
## # A tibble: 1 × 6
## .y. p p.adj p.format p.signif method
## <chr> <dbl> <dbl> <chr> <chr> <chr>
## 1 value 0.00000000283 0.0000000028 2.8e-09 **** Kruskal-Wallis
Sum of the mono-reconv does not equal the mix-reconv.
Also, increased colonization for each strain in mono-reconv than when
part of a mix.